What is an AI agent? A guide with examples and how to build one

An AI agent is software that gets to work on its own: you set a goal, the agent picks the steps, uses your tools and data, and stops only once the task is done. So it’s not a glorified chatbot, and not classic automation with fixed if-then rules either. In this article you get the clear definition, the difference with chatbots and workflows, what agentic AI means and what your SME can do with it today. We’re not writing this from theory: at ClickForest, AI agents we built ourselves with Claude Code and MCP run every day, from the chatbot and the SEO scan on this site to the agents that fact-checked this article. That hands-on experience runs through the whole piece.

What is an AI agent?

An AI agent is software in which a language model determines for itself which steps and tools it uses to reach a goal. The agent works in a loop: look at the situation, choose an action, execute it with a tool, evaluate the result and continue until the task is done. That independence sets an agent apart from an ordinary AI tool that gives a single answer per question.

Anthropic, the company behind Claude, draws the distinction most sharply:

“Workflows are systems where LLMs and tools are orchestrated through predefined code paths. Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.”

— Anthropic, engineering blog “Building Effective AI Agents”

OpenAI applies the same bar in its build guide and lists three fixed building blocks: a model that reasons and decides, tools the agent uses to actually do something (read files, send emails, update your CRM) and instructions that define how it behaves. Miss one of the three and you have a chat window, not an agent.

What is the difference between a chatbot, a workflow and an AI agent?

A chatbot answers, a workflow runs a fixed script and an AI agent decides for itself. With a chatbot and a workflow every step is set in advance; an agent chooses its own path per situation and uses tools where needed. That lets an agent complete tasks too variable for classic automation.

	Chatbot	Workflow (n8n, Make, Zapier)	AI agent
What it does	Answers questions in a conversation	Runs a pre-drawn set of steps	Completes a goal and picks its own steps
Who sets the steps	The script or knowledge base	You, in advance, step by step	The language model, while working
Fails on	Questions outside the script	Exceptions to the fixed path	Vague goals or missing access
Typical example	FAQ bot on your website	Order pushed automatically to your accounting tool	Agent that researches, looks up and resolves a customer query

Mind you: the terms get abused freely in marketing material. Gartner counted thousands of vendors calling themselves “agentic”, but estimated that only around 130 of them offer genuine agent capabilities. They have a word for it: agent washing. An AI chatbot for customer service can be perfectly valuable without being an agent; it only becomes misleading when a renamed FAQ bot is sold as an autonomous employee.

What is agentic AI and is it the same as an AI agent?

Agentic AI is the umbrella term for AI systems that complete complex tasks with little or no human supervision. An AI agent is the concrete building block of such a system. In practice both terms are used interchangeably: anyone talking about agentic AI is talking about software that runs on AI agents.

Deloitte describes agentic AI as software that “can complete complex tasks and meet objectives with little or no human supervision”, and predicted in late 2024 that half of the companies using generative AI would run agentic pilots by 2027. Gartner expects that by 2028 at least 15% of day-to-day work decisions will be made autonomously by agentic AI and that a third of enterprise software will include agentic features. OpenAI CEO Sam Altman summed up the expectation in early 2025:

“We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies.”

— Sam Altman, CEO of OpenAI

There’s a mirror image too: agents that don’t work for you but come to buy from you. How to keep your webshop findable for those buying agents is covered in our article on agentic commerce.

What can an SME do with AI agents today?

More than you’d think: answering customer queries, preparing quotes, drafting reports, checking content, following up orders and pulling data from different systems together. The rule of thumb: any task that recurs, runs digitally and has a checkable outcome is a candidate. At ClickForest we run agents for exactly that every day, from chatbot to fact-check.

Concretely from our own practice: the chatbot on this site is built on the Claude ecosystem and answers visitor questions from our own knowledge base, our SEO/GEO scan analyses websites autonomously, our CRM has a self-built MCP connection so agents process quotes and customer data directly inside it, and every blog article passes a verification agent that checks figures and quotes against the source. ClickForest builds these AI agents for SMEs in Flanders too, always starting from one concrete task, and deploys them from marketing automation to customer-service chatbots.

The figures show the timing is right. According to Eurostat, in 2025 already 34.5% of Belgian companies with at least ten employees used AI technologies, well above the EU average of 20%. But mind the difference between using AI and deploying agents: McKinsey found globally in late 2025 that 62% of organisations experiment with agents, while per business function no more than 10% actually scale them. The gap between trying and doing is precisely where an SME builds a lead now. Anthropic is aiming at exactly that, with president Daniela Amodei putting it this way at the launch of their small-business offering:

“Small businesses make up nearly half the American economy, but they’ve never had the resources of bigger companies. AI is the first technology that can finally close that gap.”

— Daniela Amodei, co-founder and president of Anthropic

How do you build an AI agent yourself with Claude Code and MCP?

You need three building blocks: a strong language model, access to your tools and data, and clear instructions. Claude Code delivers the agent loop ready-made, MCP securely connects your own systems, and Skills give the agent reusable methods. Heavier infrastructure isn’t needed for most SME tasks.

Here’s how the pieces fit together:

• Claude Code is the agent environment: it runs in your terminal, editor or browser, can read and write files, run commands and search the web, and splits big work across parallel sub-agents.
• MCP (Model Context Protocol) is the power strip between agent and systems. Since December 2025 it has been an open industry standard under the Linux Foundation, with more than 10,000 public MCP servers and adoption by ChatGPT, Gemini, Microsoft Copilot and VS Code, among others. One connection, every platform.
• Claude Skills are folders with instructions and scripts the agent picks up itself when a task calls for it: your house style, your quote format, your review checklist. Skills grew into an open format that other agent tools adopted in late 2025 too.
• The Claude Agent SDK gives developers the same agent loop as a standalone building block in Python or TypeScript, for agents that run entirely inside your own product or backend.

No developer in-house? Then Claude Cowork is the entry point: the same agent engine, wrapped in a regular interface next to Claude Desktop. AI researcher Simon Willison put it aptly:

“As far as I can tell Claude Cowork is regular Claude Code wrapped in a less intimidating default interface and with a filesystem sandbox configured for you without you needing to know what a ‘filesystem sandbox’ is.”

— Simon Willison, independent AI researcher and maker of Datasette

That non-technical use is no niche: figures TechCrunch published in July 2026 show that over 90% of Cowork work is not software development, with business processes and content production leading. Would you rather have this built than figure it out yourself? Then see how we approach building with AI.

Want to know which task in your business is agent-ready first? ClickForest builds AI agents for SMEs in Flanders, from task analysis to a working agent with human control. Discover the AI agents service.

Do you need n8n or Make to build an AI agent?

No. n8n and Make are workflow platforms: handy for connecting apps with fixed steps, but the agent intelligence comes from the language model, not the platform. Whoever builds directly with Claude Code and MCP has fewer links and more control. Choose no-code mainly if you only want to connect existing apps.

Almost every guide online starts at a workflow canvas, and for classic automation that’s fair: n8n shows every reasoning step visibly on the canvas and deliberately keeps a human in the loop, strong for teams that want to work visually. But there’s a layer between you and the model, with its own subscriptions, its own limits and an extra place where your data passes. Since MCP became an open standard, an agent can talk to your systems directly and securely, without a workflow platform in between. Anthropic’s own build advice points the same way:

“Consistently, the most successful implementations use simple, composable patterns rather than complex frameworks.”

— Anthropic, engineering blog “Building Effective AI Agents”

Our line at ClickForest: goal and data first, then the tooling. For most agent tasks Claude and MCP are enough; an overview of AI tools for marketers helps you sharpen the rest of your stack.

What does an AI agent cost for an SME?

The cost of an AI agent depends on three factors: the subscription to the underlying model, the build time for the connections to your systems and the management afterwards. For a first agent on a well-defined task that stays modest for most SMEs; the real investment sits in getting the task sharp.

Important to know: an agent doesn’t need separate, expensive infrastructure. Claude Code is simply included in the regular Claude subscriptions, and MCP connections are open source. So the question isn’t so much what an agent costs, but what the task costs you today. The vendor cases give an indication of the upper limit of what’s possible: pharma giant Novo Nordisk says clinical study reports that used to take ten weeks or more are now produced in about ten minutes, and Rakuten reports, according to Anthropic, 79% shorter turnaround times on software releases. Take such figures as direction, not a promise; they’re self-reported results from large organisations.

“Claude has helped us cut writing times on CSRs by 90% so we can get documentation directly into human hands for review and approval.”

— Waheed Jowiya, Digitalization Strategy Director at Novo Nordisk

How to budget such a track as an SME without overreaching is also covered in our guide on AI consultancy for SMEs. If you would rather have it built, the overview of Belgian agencies that build AI agents shows which eleven demonstrably offer that service, and which two questions separate a real agent from a chatbot with a new label.

What are the risks and pitfalls of AI agents?

The biggest risks are inflated expectations, unclear business value and blind trust. Gartner predicts that over 40% of agentic AI projects will fail by the end of 2027. The remedy: start small with a measurable task, build in human control and choose tools that show what the agent does and why.

“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied.”

— Anushree Verma, Senior Director Analyst at Gartner

The pitfalls in a row:

• Starting from the technology instead of the task. An agent without a clear goal is an expensive demo.
• Autonomy without control. Let an agent run alongside human approval for weeks before it’s allowed to execute on its own. We work that way ourselves: every agent output passes a checkpoint until the error margin is provably low.
• Treating GDPR as an afterthought. Decide in advance which data the agent sees, where it’s processed and who approves decisions involving personal data.
• Buying agent washing. Ask vendors what the system decides itself. If the answer is “it follows your steps”, you’re buying a workflow, not an agent.

How do you get started as an SME this month?

Pick one recurring task with clear input and output, give an agent the access it needs, let it run for a week under human control and measure the difference. If it works, you expand to a next task. If it doesn’t, you’ve spent a few days at most learning what needs to improve.

The plan we follow ourselves:

1. List your recurring tasks and score them on volume, error-proneness and how clear the desired outcome is.
2. Pick the most boring task with the clearest outcome. Not the most spectacular one: the most boring. That’s where the fastest return sits.
3. Give the agent access to exactly the data and tools it needs, no more. MCP makes that access scopable.
4. Run a week in the shadows: the agent works, a human approves. Measure time, quality and errors against the old way.
5. Scale per task, not per department. Every new task gets the same trial period.

What should you remember about AI agents?

An AI agent isn’t a smarter chatbot but a different category of software: a language model that completes tasks on its own with tools and instructions. Agentic AI is the broader term for that shift, and the building blocks are open and accessible today: Claude Code for the agent loop, MCP as an open standard to your own systems, Skills for your methods, Cowork for those who don’t code. You don’t need a workflow platform for it, you need a well-chosen task and human control. Looking for a partner to build such an agent or chatbot for you? The overview of Belgian AI chatbot agencies compares nine players per segment.

Belgium sits in the European front group for AI use, but real agent deployment is still at the start everywhere. Whoever automates one task well now builds a lead while the competition is still watching demos. As a specialist in AI agents for SMEs in Flanders, ClickForest helps you pick that first task, build the agent and keep control tight. See the AI agents service or book a no-obligation video call and find out which task in your business is the first to qualify.